# Manipulation-Oriented Object Perception in Clutter through Affordance Coordinate Frames

arXiv: https://arxiv.org/abs/2010.08202 · HTML: https://mlsanity.com/paper/2010.08202/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [GenDP: 3D Semantic Fields for Category-Level Generalizable Diffusion Policy](https://mlsanity.com/paper/2410.17488/) (2024)
- [Where To Start? Transferring Simple Skills to Complex Environments](https://mlsanity.com/paper/2212.06111/) (2022)
- [TransNet: Transparent Object Manipulation Through Category-Level Pose Estimation](https://mlsanity.com/paper/2307.12400/) (2023)
- [Beyond World-Frame Action Heads: Motion-Centric Action Frames for Vision-Language-Action Models](https://mlsanity.com/paper/2605.11809/) (2026)

## Abstract (arXiv)

In order to enable robust operation in unstructured environments, robots should be able to generalize manipulation actions to novel object instances. For example, to pour and serve a drink, a robot should be able to recognize novel containers which afford the task. Most importantly, robots should be able to manipulate these novel containers to fulfill the task. To achieve this, we aim to provide robust and generalized perception of object affordances and their associated manipulation poses for reliable manipulation. In this work, we combine the notions of affordance and category-level pose, and introduce the Affordance Coordinate Frame (ACF). With ACF, we represent each object class in terms of individual affordance parts and the compatibility between them, where each part is associated with a part category-level pose for robot manipulation. In our experiments, we demonstrate that ACF outperforms state-of-the-art methods for object detection, as well as category-level pose estimation for object parts. We further demonstrate the applicability of ACF to robot manipulation tasks through experiments in a simulated environment.